EEGdash›NeMAR›NM000296

Iss. 296 · 30 subjects · 360 recordings · CC-BY-4.0

Dataset Brief · MIMED2024

NM000296: eeg dataset, 30 subjects#

MIMED2024: Motor Imagery MIMED dataset from Wirawan et al. 2024

Access recordings and metadata through EEGDash.

Citation: I Made Agus Wirawan, Dechrit Maneetham, I Gede Mahendra Darmawiguna, Arnon Niyomphol, Pakornkiat Sawetmethikul, Padma Nyoman Crisnapati, Yamin Thwe, Ni Nyoman Mestri Agustini (2024). MIMED2024: Motor Imagery MIMED dataset from Wirawan et al. 2024. 10.82901/nemar.nm000296

Modality: eeg Subjects: 30 Recordings: 360 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

30-participant EEG dataset — MIMED2024: Motor Imagery MIMED dataset from Wirawan et al. 2024.

Data & curation I Made Agus Wirawan · Dechrit Maneetham · I Gede Mahendra Darmawiguna · Arnon Niyomphol · Pakornkiat Sawetmethikul · Padma Nyoman Crisnapati · …
Senior author Ni Nyoman Mestri Agustini
Year 2024 · Distributed via NeMAR

EEG · 14 ch128 HzBIDS 1.9.0Task · imagery2 sessions

Layer 01Study

What was asked

Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.

Layer 02Signal · BIDS

What was recorded

Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.

Layer 03Training · ML

What you can train on

Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.

§ 01Access · Get started

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000296

dataset = NM000296(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Query & Filter

Filter by subject

dataset = NM000296(cache_dir="./data", subject="01")

Advanced query

dataset = NM000296(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

Cite This Dataset

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{nm000296,
  title = {MIMED2024: Motor Imagery MIMED dataset from Wirawan et al. 2024},
  author = {I Made Agus Wirawan and Dechrit Maneetham and I Gede Mahendra Darmawiguna and Arnon Niyomphol and Pakornkiat Sawetmethikul and Padma Nyoman Crisnapati and Yamin Thwe and Ni Nyoman Mestri Agustini},
  doi = {10.82901/nemar.nm000296},
  url = {https://doi.org/10.82901/nemar.nm000296},
}

§ 02Study · The README

About This Dataset#

Motor Imagery MIMED dataset from Wirawan et al. 2024 [1]_.

Code: MIMED2024

Paradigm: imagery DOI: 10.1016/j.dib.2024.110833 Subjects: 30 Sessions per subject: 2 Events: left_hand=1, right_hand=2, trunk=3 Trial interval: [0, 4] s Runs per session: 6 File format: MAT (converted from EDF)

DOI

MIMED2024

Acquisition

Sampling rate: 128.0 Hz Number of channels: 14 Channel types: eeg=14 Channel names: AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8, AF4

View full README

DOI

MIMED2024

Acquisition

Sampling rate: 128.0 Hz Number of channels: 14 Channel types: eeg=14 Channel names: AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8, AF4 Montage: standard_1020 Hardware: Emotiv EPOC X Reference: CMS/DRL (P3/P4) Ground: DRL Sensor type: saline felt (Ag/AgCl) Line frequency: 50.0 Hz

Participants

Number of subjects: 30 Health status: healthy Gender distribution: male=16, female=14

Experimental Protocol

Paradigm: imagery Number of classes: 3 Class labels: left_hand, right_hand, trunk Stimulus type: video Mode: offline

HED Event Annotations

Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser left_hand

     ├─ Sensory-event, Experimental-stimulus, Visual-presentation
     └─ Agent-action
        └─ Imagine
           ├─ Move
           └─ Left, Hand

right_hand

     ├─ Sensory-event, Experimental-stimulus, Visual-presentation
     └─ Agent-action
        └─ Imagine
           ├─ Move
           └─ Right, Hand

trunk

├─ Sensory-event
└─ Label/trunk

Tags

Modality: Motor Type: Motor Imagery

Documentation

Description: MIMED: motor imagery and motor execution EEG dataset of six activities recorded from 30 subjects with an Emotiv EPOC X 14-channel headset at 128 Hz. DOI: 10.1016/j.dib.2024.110833 License: CC-BY-4.0 Investigators: I Made Agus Wirawan, Dechrit Maneetham, I Gede Mahendra Darmawiguna, Arnon Niyomphol, Pakornkiat Sawetmethikul, Padma Nyoman Crisnapati, Yamin Thwe, Ni Nyoman Mestri Agustini Institution: Universitas Pendidikan Ganesha Department: Data Science Laboratories, Engineering and Vocational Faculty Country: ID Repository: Mendeley Data Data URL: https://doi.org/10.17632/zs25xxjkm9.3 Publication year: 2024

Abstract

The MIMED dataset provides EEG recordings of motor imagery and motor execution for six activities (raising and lowering each hand, standing and sitting) from 30 subjects, acquired with a 14-channel Emotiv EPOC X headset at 128 Hz. The distributed imagery .mat files carry no per-repetition activity label, so the loader exposes the reliable folder-level 3-class task (left_hand / right_hand / trunk).

References

Wirawan, I. M. A., et al. (2024). Acquisition and processing of Motor Imagery and Motor Execution Dataset (MIMED) for six movement activities. Data in Brief, 56, 110833. DOI: https://doi.org/10.1016/j.dib.2024.110833 .. versionadded:: 1.8 Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Hochenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896 Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8 Generated by MOABB 1.8.0dev0 (Mother of All BCI Benchmarks) NeuroTechX/moabb

Ethics

Ethics approval: the data analysed in this deposit were collected under the ethics approval obtained by the original investigators and reported in the primary publication cited above (see References/Documentation sections of this README). Participants gave informed consent in the source study. No new human-subject data were collected during this BIDS re-release; this NEMAR record only reformats the published source data into BIDS via MOABB.

Please consult the primary publication for the exact IRB/ethics committee reference.

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000296-blue)](https://doi.org/10.82901/nemar.nm000296) MIMED2024 ========= Motor Imagery MIMED dataset from Wirawan et al. 2024 [1]_. Dataset Overview —————-

Code: MIMED2024 Paradigm: imagery DOI: 10.1016/j.dib.2024.110833 Subjects: 30 Sessions per subject: 2 Events: left_hand=1, right_hand=2, trunk=3 Trial interval: [0, 4] s Runs per session: 6 File format: MAT (converted from EDF)

Acquisition#

Sampling rate: 128.0 Hz Number of channels: 14 Channel types: eeg=14 Channel names: AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8, AF4 Montage: standard_1020 Hardware: Emotiv EPOC X Reference: CMS/DRL (P3/P4) Ground: DRL Sensor type: saline felt (Ag/AgCl) Line frequency: 50.0 Hz

Participants#

Number of subjects: 30 Health status: healthy Gender distribution: male=16, female=14

Experimental Protocol#

Paradigm: imagery Number of classes: 3 Class labels: left_hand, right_hand, trunk Stimulus type: video Mode: offline

HED Event Annotations#

Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser left_hand

├─ Sensory-event, Experimental-stimulus, Visual-presentation └─ Agent-action

└─ Imagine
├─ Move └─ Left, Hand

right_hand
├─ Sensory-event, Experimental-stimulus, Visual-presentation └─ Agent-action

└─ Imagine
├─ Move └─ Right, Hand

trunk
├─ Sensory-event └─ Label/trunk

Tags#

Modality: Motor Type: Motor Imagery

Documentation#

Description: MIMED: motor imagery and motor execution EEG dataset of six activities recorded from 30 subjects with an Emotiv EPOC X 14-channel headset at 128 Hz. DOI: 10.1016/j.dib.2024.110833 License: CC-BY-4.0 Investigators: I Made Agus Wirawan, Dechrit Maneetham, I Gede Mahendra Darmawiguna, Arnon Niyomphol, Pakornkiat Sawetmethikul, Padma Nyoman Crisnapati, Yamin Thwe, Ni Nyoman Mestri Agustini Institution: Universitas Pendidikan Ganesha Department: Data Science Laboratories, Engineering and Vocational Faculty Country: ID Repository: Mendeley Data Data URL: https://doi.org/10.17632/zs25xxjkm9.3 Publication year: 2024

Abstract#

The MIMED dataset provides EEG recordings of motor imagery and motor execution for six activities (raising and lowering each hand, standing and sitting) from 30 subjects, acquired with a 14-channel Emotiv EPOC X headset at 128 Hz. The distributed imagery .mat files carry no per-repetition activity label, so the loader exposes the reliable folder-level 3-class task (left_hand / right_hand / trunk). References ———- Wirawan, I. M. A., et al. (2024). Acquisition and processing of Motor Imagery and Motor Execution Dataset (MIMED) for six movement activities. Data in Brief, 56, 110833. DOI: https://doi.org/10.1016/j.dib.2024.110833 .. versionadded:: 1.8 Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Hochenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896 Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8 — Generated by MOABB 1.8.0dev0 (Mother of All BCI Benchmarks) NeuroTechX/moabb Ethics —— Ethics approval: the data analysed in this deposit were collected under the ethics approval obtained by the original investigators and reported in the primary publication cited above (see References/Documentation sections of this README). Participants gave informed consent in the source study. No new human-subject data were collected during this BIDS re-release; this NEMAR record only reformats the published source data into BIDS via MOABB. Please consult the primary publication for the exact IRB/ethics committee reference.

License: CC-BY-4.0

Authors:

I Made Agus Wirawan
Dechrit Maneetham
I Gede Mahendra Darmawiguna
Arnon Niyomphol
Pakornkiat Sawetmethikul
… and 3 more

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000296

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 14 ch (n=360 recordings)

Sampling frequencies: 128.0 Hz (n=360 recordings)

Total recording duration: 1 h 33 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 14 ch · EEG · 128 Hz · 30 subjects, 360 recordings

Live trace viewer — sub-1 · ses-0 · task-imagery · run-0

Showing one representative recording out of 30 subjects and 360 recordings in this dataset. Browse the full set on OpenNeuro; drop any other _eeg.{set,edf,bdf,vhdr} file onto the viewer (or pass ?eeg=<url>) to inspect it.

Electrode layout — EEG · 14 sensors — 14 channels

NEMAR Processing Statistics#

The plots below are generated by NEMAR’s automated EEG pipeline. The histogram shows pipeline success for data cleaning and ICA decomposition, the percentage of data frames and EEG channels retained after artefact removal, line noise per channel (RMS, dB), and the age/gender distribution of participants.

HED event descriptors word cloud

§ 05Manifest · BIDS tree

Manifest#

File Explorer#

Browse the BIDS file structure of this dataset. Records are fetched on demand from the EEGDash catalog the first time you open the explorer.

Recordings—

Files—

Subjects—

Modalities—

Click to load file structure…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.NM000296(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000296

Sourceeegdash/dataset/registry.py · [source ↗]

class eegdash.dataset.NM000296(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

MIMED2024: Motor Imagery MIMED dataset from Wirawan et al. 2024

Study:: nm000296 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000296.

Modality: eeg; Subject type: Unknown. Subjects: 30; recordings: 360; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir#

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query#

Merged query with the dataset filter applied.

Type:: dict

records#

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000296 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000296 DOI: https://doi.org/10.82901/nemar.nm000296

Examples

>>> from eegdash.dataset import NM000296
>>> dataset = NM000296(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

__init__(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

save(path: str, overwrite: bool = False, offset: int = 0)[source]#

Save datasets to files by creating one subdirectory for each dataset:

path/
    0/
        0-raw.fif | 0-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)
    1/
        1-raw.fif | 1-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)

Parameters:

path (str) –

Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.

Access modesMNE → braindecode → PyTorch → ML

.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne

BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode

DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch

Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr

Hugging FaceNo per-dataset mirror published yet — browse the EEGDash org listing for sibling datasets. See the datasets loader API.huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — NM000296.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons

Examples using EEGDashcurated · start here

Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.

Load one EEG recordingResolve a single record to an MNE Raw with channels and events.

EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.

Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.

Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.

Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.

Swap any load_dataset(...) call for nm000296 to reproduce the tutorial on this dataset.

Citation

I Made Agus Wirawan, Dechrit Maneetham, I Gede Mahendra Darmawiguna, Arnon Niyomphol, Pakornkiat Sawetmethikul, … (2024). MIMED2024: Motor Imagery MIMED dataset from Wirawan et al. 2024. 10.82901/nemar.nm000296

Provenance

¹Contributed to nemar in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.82901/nemar.nm000296.

Related & sibling datasets

NM000324EEG · 32 subj NM000334EEG · 41 subj NM000320EEG · 10 subj NM000314EEG · 10 subj NM000280EEG · 47 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC-BY-4.0 · 10.82901/nemar.nm000296

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000296`
Title	MIMED2024: Motor Imagery MIMED dataset from Wirawan et al. 2024
Author (year)	—
Canonical	—
Importable as	`NM000296`
Year	2024
Authors	I Made Agus Wirawan, Dechrit Maneetham, I Gede Mahendra Darmawiguna, Arnon Niyomphol, Pakornkiat Sawetmethikul, Padma Nyoman Crisnapati, Yamin Thwe, Ni Nyoman Mestri Agustini
License	CC-BY-4.0
Citation / DOI	10.82901/nemar.nm000296
Source links	OpenNeuro \| NeMAR \| Source URL

NM000296: eeg dataset, 30 subjects#

Quickstart#

About This Dataset#

NEMAR Metadata#

Acquisition#

Participants#

Experimental Protocol#

HED Event Annotations#

Tags#

Documentation#

Abstract#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#